code: def test_ddpg():
      # TODO: replace this with a simpler environment where we can actually test if it finds a solution
      env = gym.make("Pendulum-v0")
      np.random.seed(123)
      env.seed(123)
      random.seed(123)
      nb_actions = env.action_space.shape[0]
  
      actor = Sequential()
      actor.add(Flatten(input_shape=(1,) + env.observation_space.shape))
      actor.add(Dense(16))
      actor.add(Activation("relu"))
      actor.add(Dense(nb_actions))
      actor.add(Activation("linear"))
  
      action_input = Input(shape=(nb_actions,), name="action_input")
      observation_input = Input(
          shape=(1,) + env.observation_space.shape,
          name="observation_input",
      )
      flattened_observation = Flatten()(observation_input)
      x = Concatenate()([action_input, flattened_observation])
      x = Dense(16)(x)
      x = Activation("relu")(x)
      x = Dense(1)(x)
      x = Activation("linear")(x)
      critic = Model(
          inputs=[action_input, observation_input], outputs=x
      )
  
      memory = SequentialMemory(limit=1000, window_length=1)
      random_process = OrnsteinUhlenbeckProcess(
          theta=0.15, mu=0.0, sigma=0.3
      )
      agent = DDPGAgent(
          nb_actions=nb_actions,
          actor=actor,
          critic=critic,
          critic_action_input=action_input,
          memory=memory,
          nb_steps_warmup_critic=50,
          nb_steps_warmup_actor=50,
          random_process=random_process,
          gamma=0.99,
          target_model_update=1e-3,
      )
      agent.compile([Adam(lr=1e-3), Adam(lr=1e-3)])
  
      agent.fit(
          env,
          nb_steps=400,
          visualize=False,
          verbose=0,
          nb_max_episode_steps=100,
      )
      h = agent.test(
          env, nb_episodes=2, visualize=False, nb_max_episode_steps=100
      )
  
file_path: keras-rl/tests/integration/test_continuous.py
imports: import random
  import numpy
  import gym
  from keras.models import Sequential, Model
  from keras.layers import Dense, Activation, Flatten, Input, Concatenate
  from keras.optimizers import Adam
  from rl.agents import NAFAgent, DDPGAgent
  from rl.random import OrnsteinUhlenbeckProcess
  from rl.memory import SequentialMemory
label_desc: Use image node_id 2 for calling a global function with example usage: test_ddpg() without return types